In recent years, UAV-BS assisted wireless communication networks have attracted significant attention from both industry and academia [1]}, [2]}, [3]}, [4]}, [5]}. To guarantee a robust wireless connection between the UAV-BSs and the core network, more and more research work has started working on improving the wireless backhaul link [6]}, [7]}, [8]}, [9]}, [10]}. Authors in [6]} assume that all the UAV-BSs are flying at a fixed height, and a robust backbone network among UAV-BSs is guaranteed by ensuring that there is always at least one path between any UAV-BS and a BS on the ground if one UAV-BS fails. Then they investigate the rapid UAV deployment problem by minimizing the number of UAVs to provide on-demand coverage for as many users as possible. In [7]}, optimal 3-D deployment of a UAV-BS is investigated to maximize the number of connected users with different service requirements by considering the limitation of wireless backhaul links. In [8]}, the limitation of backhaul and access capacities is also considered, and a heuristic algorithm is proposed to optimize the UAV navigation and bandwidth allocation. Similar to [7]}, the authors in [10]} also investigate a coverage improvement problem enabled by UAV-BS with backhaul limitation but with a machine learning (ML) based solution.
